Covariats_Wilkinson_IV
Sagitova M.K.
2025-12-03
1. Приведение данных к единому виду
Ammon_1996_clean <- Ammon_1996 %>%
mutate(case = paste(subject_id, occasion, sep = "-")) %>%
mutate(route = route %>% str_remove_all(" infusion")) %>%
rename(infusion_duration_min = duration_inf_min)
rm(Ammon_1996)Jones_1984_clean <- Jones_1984 %>%
mutate(subject_id = paste("J", subject_id, sep = "-"),
sex = case_when(sex == "male" ~ "M",
sex == "female" ~ "F"))
rm(Jones_1984)Yelland_2008_clean <- Yelland_2008 %>%
mutate(case = paste(subject_id, occasion, sep = "-"))
rm(Yelland_2008)Emberger_2023_clean <- Emberger_2023 %>%
filter(!is.na(conc_g_per_L)) %>%
rename(wt_kg = WT_kg,
sex = Sex,
age = Age,
bh_cm = BH_cm,
bmi = BMI) %>%
mutate(subject_id = str_c("TE", "-", subject_id))
rm(Emberger_2023)Wilkinson_IV_clean <- Wilkinson_IV %>%
filter(!is.na(conc_g_per_L)) %>%
mutate(route = route %>% str_remove_all(" infusion")) %>%
rename(wt_kg = WT_kg,
sex = Sex)
rm(Wilkinson_IV)Wilkinson_PO_clean <- Wilkinson_PO %>%
filter(!is.na(conc_g_per_L)) %>%
mutate(case = paste(subject_id, occasion, sep = "-")) %>%
rename(wt_kg = WT_kg,
sex = Sex,
bh_cm = BH_cm,
bsa_m2 = BSA_m2,
age = Age)
rm(Wilkinson_PO)Vestal_1977_e_pk <- Vestal_1977_elderly %>%
rename_with(tolower) %>%
rename(Cmax = cmax_mg_dl) %>%
mutate(Cmax = Cmax / 100,
route = case_when(
str_detect(str_to_lower(route), "iv") ~ "IV",
str_detect(str_to_lower(route), "po") ~ "PO"),
subject_id = str_c("V-E-", subject_id))
rm(Vestal_1977_elderly)Vestal_1977_y_pk <- Vestal_1977_young %>%
rename_with(tolower) %>%
rename(Cmax = cmax_mg_dl) %>%
mutate(Cmax = Cmax / 100,
route = case_when(
str_detect(str_to_lower(route), "iv") ~ "IV",
str_detect(str_to_lower(route), "po") ~ "PO"),
subject_id = str_c("V-Y-", subject_id))
rm(Vestal_1977_young)2. Графики по датасетам
2.1 Графики индивидуальных кривых
Ammon_1996_clean %>%
select(subject_id, conc_g_per_L, time_min, occasion) %>%
mutate(occasion = occasion %>% as.factor) %>%
ggplot(aes(color = occasion)) +
geom_line(aes(x = time_min, y = conc_g_per_L, group = occasion), linewidth = 2) +
facet_wrap(subject_id~.)+
scale_x_continuous(breaks = seq(0, 420, by = 50))+
scale_y_continuous(breaks = seq(0, 1, by = 0.25))+
labs(title = "Зависимость концентрации от времени", subtitle = "Индивидуальные кривые для каждого субъекта", x = "Время, мин", y = "Концентрация, г/л", color = "Визит", caption = "Ammon-1996")+
theme(legend.position = "top")
ggplot(Jones_1984_clean, aes(x = time_min, y = conc_g_per_L)) +
geom_line(aes(group = subject_id)) +
scale_y_continuous(breaks = seq(0, 1.4, 0.1)) +
scale_x_continuous(breaks = seq(0, 420, 30)) +
# coord_cartesian(xlim = c(60, 420)) +
labs(title = "Зависимость концентрации от времени",
subtitle = "Индивидуальные кривые для каждого субъекта",
caption = "Jones-1984",
x = "Время, мин",
y = "Концентрация, г/л")
Yelland_2008_clean %>%
select(subject_id, conc_g_per_L, time_min, occasion) %>%
mutate(occasion = occasion %>% as.factor) %>%
ggplot(aes(color = occasion)) +
geom_line(aes(x = time_min, y = conc_g_per_L, group = occasion), linewidth = 2) +
facet_wrap(subject_id~.)+
scale_x_continuous(breaks = seq(0, 420, by = 50))+
scale_y_continuous(breaks = seq(0, 1, by = 0.25))+
labs(title = "Зависимость концентрации от времени", subtitle = "Индивидуальные кривые для каждого субъекта", x = "Время, мин", y = "Концентрация, г/л", color = "Визит", caption = "Yelland-2008")+
theme(legend.position = "top")
Emberger_2023_clean %>%
select(subject_id, conc_g_per_L, time_min) %>%
mutate(subject_id = subject_id %>% str_remove_all("TE-")) %>%
ggplot(aes(color = subject_id)) +
geom_line(aes(x = time_min, y = conc_g_per_L, group = subject_id), linewidth = 2) +
scale_x_continuous(breaks = seq(0, 420, by = 50)) +
scale_y_continuous(breaks = seq(0, 1, by = 0.25)) +
scale_color_discrete(breaks = as.character(1:10)) +
labs(title = "Зависимость концентрации от времени",
subtitle = "Индивидуальные кривые для каждого субъекта",
x = "Время, мин", y = "Концентрация, г/л",
color = "Субъект",
caption = "По данным Thierauf-Emberger-2023") +
theme(legend.position = "top")
ggplot(Wilkinson_IV_clean, aes(x = time_min, y = conc_g_per_L)) +
geom_line(aes(group = subject_id)) +
scale_y_continuous(breaks = seq(0, 1.4, 0.1)) +
scale_x_continuous(breaks = seq(0, 510, 30)) +
# coord_cartesian(xlim = c(60, 420)) +
labs(title = "Зависимость концентрации от времени",
subtitle = "Индивидуальные кривые для каждого субъекта",
caption = "Wilkinson-1976-IV",
x = "Время, мин",
y = "Концентрация, г/л")
Wilkinson_PO_clean %>%
select(subject_id, conc_g_per_L, time_min, occasion) %>%
mutate(occasion = occasion %>% as.factor) %>%
ggplot(aes(color = occasion)) +
geom_line(aes(x = time_min, y = conc_g_per_L, group = occasion), linewidth = 2) +
facet_wrap(subject_id~.)+
scale_x_continuous(breaks = seq(0, 420, by = 50))+
scale_y_continuous(breaks = seq(0, 1, by = 0.25))+
labs(title = "Зависимость концентрации от времени", subtitle = "Индивидуальные кривые для каждого субъекта", x = "Время, мин", y = "Концентрация, г/л", color = "Визит", caption = "По данным Wilkinson-1976-PO")+
geom_hline(aes(yintercept = 0.2)) +
theme(legend.position = "top")
2.2 Среднеее и стандартное отклонение концентрации по времени
Время усреднено
Ammon_1996_clean %>%
group_by(case) %>%
mutate(point_id = row_number() %>% as.factor) %>%
ungroup() %>%
mutate(occasion = occasion %>% as.factor) %>%
group_by(occasion, point_id) %>%
summarise(conc_avg = mean(conc_g_per_L), conc_max = mean(conc_g_per_L) + sd(conc_g_per_L), conc_min = mean(conc_g_per_L) - sd(conc_g_per_L), time_min_avg = mean(time_min)) %>%
ungroup() %>%
ggplot() +
geom_line(aes(x = time_min_avg, y = conc_avg, group = occasion, color = occasion)) +
geom_errorbar(aes(x = time_min_avg, ymin = conc_min, ymax = conc_max), width = 6, linewidth = 0.5) +
scale_x_continuous(breaks = seq(0, 400, by = 50)) +
scale_y_continuous(breaks = seq(0, 1, by = 0.25)) +
labs(title = "Зависимость концентрации от времени",
subtitle = "Mean ± SD",
x = "Время, мин",
y = "Концентрация, г/л",
color = "Визит",
caption = "По данным Ammon-1996")
Jones_1984_clean %>%
group_by(subject_id) %>%
mutate(point_id = row_number() %>% as.factor) %>%
ungroup() %>%
group_by(point_id) %>%
summarise(conc_avg = mean(conc_g_per_L), conc_max = mean(conc_g_per_L) + sd(conc_g_per_L), conc_min = mean(conc_g_per_L) - sd(conc_g_per_L), time_min_avg = mean(time_min)) %>%
ggplot() +
geom_line(aes(x = time_min_avg, y = conc_avg)) +
geom_errorbar(aes(x = time_min_avg, ymin = conc_min, ymax = conc_max), width = 6, linewidth = 0.5) +
scale_x_continuous(breaks = seq(0, 400, by = 50)) +
scale_y_continuous(breaks = seq(0, 1, by = 0.25)) +
labs(title = "Зависимость концентрации от времени",
subtitle = "Mean ± SD",
x = "Время, мин",
y = "Концентрация, г/л",
caption = "По данным Jones-1984")
Yelland_2008_clean %>%
group_by(case) %>%
mutate(point_id = row_number() %>% as.factor) %>%
ungroup() %>%
mutate(occasion = occasion %>% as.factor) %>%
group_by(occasion, point_id) %>%
summarise(conc_avg = mean(conc_g_per_L), conc_max = mean(conc_g_per_L) + sd(conc_g_per_L), conc_min = mean(conc_g_per_L) - sd(conc_g_per_L), time_min_avg = mean(time_min)) %>%
ungroup() %>%
ggplot() +
geom_line(aes(x = time_min_avg, y = conc_avg, group = occasion, color = occasion)) +
geom_errorbar(aes(x = time_min_avg, ymin = conc_min, ymax = conc_max), width = 6, linewidth = 0.5) +
scale_x_continuous(breaks = seq(0, 400, by = 50)) +
scale_y_continuous(breaks = seq(0, 1, by = 0.25)) +
labs(title = "Зависимость концентрации от времени",
subtitle = "Mean ± SD",
x = "Время, мин",
y = "Концентрация, г/л",
color = "Визит",
caption = "По данным Yelland-2008")
Emberger_2023_clean %>%
group_by(subject_id) %>%
mutate(point_id = row_number() %>% as.factor) %>%
ungroup() %>%
group_by(point_id) %>%
summarise(conc_avg = mean(conc_g_per_L), conc_max = mean(conc_g_per_L) + sd(conc_g_per_L), conc_min = mean(conc_g_per_L) - sd(conc_g_per_L), time_min_avg = mean(time_min)) %>%
ggplot() +
geom_line(aes(x = time_min_avg, y = conc_avg)) +
geom_errorbar(aes(x = time_min_avg, ymin = conc_min, ymax = conc_max), width = 6, linewidth = 0.5) +
scale_x_continuous(breaks = seq(0, 400, by = 50)) +
scale_y_continuous(breaks = seq(0, 1, by = 0.25)) +
labs(title = "Зависимость концентрации от времени",
subtitle = "Mean ± SD",
x = "Время, мин",
y = "Концентрация, г/л",
caption = "По данным Thierauf-Emberger-2023")
Wilkinson_IV_clean %>%
group_by(subject_id) %>%
mutate(point_id = row_number() %>% as.factor) %>%
ungroup() %>%
group_by(point_id) %>%
summarise(conc_avg = mean(conc_g_per_L), conc_max = mean(conc_g_per_L) + sd(conc_g_per_L), conc_min = mean(conc_g_per_L) - sd(conc_g_per_L), time_min_avg = mean(time_min)) %>%
filter(as.numeric(point_id) < 27) %>%
ggplot() +
geom_line(aes(x = time_min_avg, y = conc_avg)) +
geom_errorbar(aes(x = time_min_avg, ymin = conc_min, ymax = conc_max), width = 6, linewidth = 0.5) +
scale_x_continuous(breaks = seq(0, 400, by = 50)) +
scale_y_continuous(breaks = seq(0, 1, by = 0.25)) +
labs(title = "Зависимость концентрации от времени",
subtitle = "Mean ± SD",
x = "Время, мин",
y = "Концентрация, г/л",
caption = "По данным Wilkinson-1976-IV") 
Wilkinson_PO_clean %>%
group_by(case) %>%
mutate(point_id = row_number() %>% as.factor) %>%
ungroup() %>%
group_by(point_id) %>%
summarise(conc_avg = mean(conc_g_per_L), conc_max = mean(conc_g_per_L) + sd(conc_g_per_L), conc_min = mean(conc_g_per_L) - sd(conc_g_per_L), time_min_avg = mean(time_min)) %>%
ggplot() +
geom_line(aes(x = time_min_avg, y = conc_avg)) +
geom_errorbar(aes(x = time_min_avg, ymin = conc_min, ymax = conc_max), width = 6, linewidth = 0.5) +
scale_x_continuous(breaks = seq(0, 400, by = 50)) +
scale_y_continuous(breaks = seq(0, 1, by = 0.25)) +
labs(title = "Зависимость концентрации от времени",
subtitle = "Mean ± SD",
x = "Время, мин",
y = "Концентрация, г/л",
caption = "По данным Wilkinson-1976-PO") 
3. Расчет ФК параметров для датасетов
#k - это насколько мы прибавляем к индексу tmax
pharma_fun <- function(conc, t, dose, k = 1) {
# Моделирование для подсчёта beta, C0, Vd 0 порядка
t_sample <- {{t}}[(first(which.max({{conc}})) + k):length({{t}})]
conc_sample <- conc[(first(which.max({{conc}})) + k):length({{conc}})]
dose <- first({{dose}})
model <- lm(conc_sample ~ t_sample)
Beta <- -as.numeric(model$coefficients[2])
C_0_0_order <- as.numeric(model$coefficients[1])
V_d_0_order <- dose / C_0_0_order
# Моделирование для подсчёта kel, C0, Vd 1 порядка
indexes <- conc_sample > 0
model_1 <- lm(log(conc_sample[indexes]) ~ t_sample[indexes])
Kel <- -as.numeric(model_1$coefficients[2])
C_0_1_order <- exp(as.numeric(model_1$coefficients[1]))
V_d_1_order <- dose / C_0_1_order
# Подсчёт AUC0-t
# if (length({{t}}) < 2) AUC <- NA
# if (anyNA({{t}}) | anyNA({{conc}})) AUC <- NaN
t0 = {{t}}[-length({{t}})]
t1 = {{t}}[-1]
c0 = {{conc}}[-length({{conc}})]
c1 = {{conc}}[-1]
AUC = sum(((c0 + c1)/2)*(t1 - t0))
# Подсчёт Cmax
cmax <- max(conc)
# Подсчёт Tmax
tmax <- t[first(which.max(conc))]
return(list(Beta = Beta,
Kel = Kel,
C_0_0_order = C_0_0_order,
V_d_0_order = V_d_0_order,
C_0_1_order = C_0_1_order,
V_d_1_order = V_d_1_order,
AUC = AUC,
Cmax = cmax,
Tmax = tmax))
}
stat_ph <- list(
"Среднее" = ~mean(., na.rm = TRUE) %>% round(4),
"Медиана" = ~median(., na.rm = TRUE)%>% round(4),
"Стандартное отклонение" = ~sd(., na.rm = TRUE) %>% round(4),
"Коэффициент вариации (CV), %" = ~round((sd(., na.rm = TRUE))/(mean(., na.rm = TRUE)) * 100, 2)
)Jones_1984_pk <- Jones_1984_clean %>%
arrange(time_min) %>%
nest(.by = subject_id, .key = "subject_data") %>%
mutate(pharma = purrr::map(subject_data,
~pharma_fun(.$conc_g_per_L,
.$time_min,
.$dose_g_per_kg))) %>%
unnest_wider(col = pharma) %>%
select(-subject_data)
Jones_1984_pk <- Jones_1984_clean %>%
select(-c(time_min, conc_g_per_L)) %>%
group_by(subject_id) %>%
slice(1) %>%
ungroup() %>%
right_join(Jones_1984_pk)## Joining with `by = join_by(subject_id)`rm(Jones_1984_clean)Ammon_1996_pk <- Ammon_1996_clean %>%
arrange(time_min) %>%
nest(.by = case, .key = "subject_data") %>%
mutate(pharma = purrr::map(subject_data,
~pharma_fun(.$conc_g_per_L,
.$time_min,
.$dose_g_per_kg))) %>%
unnest_wider(col = pharma) %>%
select(-subject_data)
Ammon_1996_pk <- Ammon_1996_clean %>%
select(-c(time_min, conc_g_per_L)) %>%
group_by(case) %>%
slice(1) %>%
ungroup() %>%
right_join(Ammon_1996_pk, by = join_by(case)) %>%
mutate(subject_id = str_c("A-IV-", subject_id)) %>%
select(-case)
rm(Ammon_1996_clean)Yelland_2008_pk <- Yelland_2008_clean %>%
arrange(time_min) %>%
nest(.by = case, .key = "subject_data") %>%
mutate(pharma = purrr::map(subject_data,
~pharma_fun(.$conc_g_per_L,
.$time_min,
.$dose_g_per_kg))) %>%
unnest_wider(col = pharma) %>%
select(-subject_data)
Yelland_2008_pk <- Yelland_2008_clean %>%
select(-c(time_min, conc_g_per_L)) %>%
group_by(case) %>%
slice(1) %>%
ungroup() %>%
right_join(Yelland_2008_pk, by = join_by(case)) %>%
mutate(subject_id = str_c("Y-", subject_id)) %>%
select(-case)
rm(Yelland_2008_clean)Emberger_2023_pk <- Emberger_2023_clean %>%
arrange(time_min) %>%
nest(.by = subject_id, .key = "subject_data") %>%
mutate(pharma = purrr::map(subject_data,
~pharma_fun(.$conc_g_per_L,
.$time_min,
.$dose_g_per_kg))) %>%
unnest_wider(col = pharma) %>%
select(-subject_data)
Emberger_2023_pk <- Emberger_2023_clean %>%
select(-c(time_min, conc_g_per_L)) %>%
group_by(subject_id) %>%
slice(1) %>%
ungroup() %>%
right_join(Emberger_2023_pk)## Joining with `by = join_by(subject_id)`rm(Emberger_2023_clean)Wilkinson_IV_pk <- Wilkinson_IV_clean %>%
arrange(time_min) %>%
nest(.by = subject_id, .key = "subject_data") %>%
mutate(pharma = purrr::map(subject_data,
~pharma_fun(.$conc_g_per_L,
.$time_min,
.$dose_g_per_kg))) %>%
unnest_wider(col = pharma) %>%
select(-subject_data)
Wilkinson_IV_pk <- Wilkinson_IV_clean %>%
select(-c(time_min, conc_g_per_L)) %>%
group_by(subject_id) %>%
slice(1) %>%
ungroup() %>%
right_join(Wilkinson_IV_pk) %>%
mutate(subject_id = str_c("W-IV-", subject_id))## Joining with `by = join_by(subject_id)`rm(Wilkinson_IV_clean)Wilkinson_PO_pk <- Wilkinson_PO_clean %>%
arrange(time_min) %>%
nest(.by = case, .key = "subject_data") %>%
mutate(pharma = purrr::map(subject_data,
~pharma_fun(.$conc_g_per_L,
.$time_min,
.$dose_g_per_kg))) %>%
unnest_wider(col = pharma) %>%
select(-subject_data)
Wilkinson_PO_pk <- Wilkinson_PO_clean %>%
select(-c(time_min, conc_g_per_L)) %>%
group_by(case) %>%
slice(1) %>%
ungroup() %>%
right_join(Wilkinson_PO_pk, by = join_by(case)) %>%
mutate(subject_id = str_c("W-PO-", subject_id)) %>%
select(-case)
rm(Wilkinson_PO_clean)4. Объединение датасетов
main_df <- bind_rows(Ammon_1996_pk,
Emberger_2023_pk,
Jones_1984_pk,
Wilkinson_IV_pk,
Wilkinson_PO_pk,
Yelland_2008_pk,
Vestal_1977_e_pk,
Vestal_1977_y_pk)
main_df <- main_df %>%
mutate(across(c(where(is.character), occasion, infusion_duration_min, -subject_id), ~as.factor(.x)))# Оставил только средние значения по визитам (независимость наблюдений соблюдена)
WILK <- main_df %>%
filter(study_id == "Wilkinson-1976-PO") %>%
arrange(desc(dose_g_per_kg), .by_group = TRUE) %>%
group_by(subject_id) %>%
slice(1)
order_0_df <- main_df %>%
select(-c(Kel, contains("1_order"))) %>%
filter(study_id != "Wilkinson-1976-PO") %>%
group_by(subject_id) %>%
mutate(across(c(Beta, C_0_0_order, V_d_0_order, AUC, Cmax, Tmax), ~mean(.x))) %>%
slice(1) %>%
ungroup() %>%
select(-occasion) %>%
bind_rows(WILK)
# nrow(order_0_df)
# length(unique(order_0_df$subject_id))
order_1_df <- main_df %>% filter(dose_g_per_kg < 0.23)main_df %>%
select(-subject_id) %>%
gtsummary::tbl_summary()| Characteristic | N = 2061 |
|---|---|
| study_id | |
| Ammon_1996 | 24 (12%) |
| Jones_1984 | 48 (23%) |
| Thierauf_Emberger_2023 | 10 (4.9%) |
| Vestal-1977-elderly | 25 (12%) |
| Vestal-1977-young | 25 (12%) |
| Wilkinson-1976-IV | 6 (2.9%) |
| Wilkinson-1976-PO | 32 (16%) |
| Yelland_2008 | 36 (17%) |
| occasion | |
| 1 | 32 (35%) |
| 2 | 32 (35%) |
| 3 | 20 (22%) |
| 4 | 8 (8.7%) |
| Unknown | 114 |
| dose_g_per_kg | 0.57 (0.52, 0.68) |
| route | |
| IV | 80 (39%) |
| PO | 126 (61%) |
| infusion_duration_min | |
| 60 | 74 (93%) |
| 120 | 6 (7.5%) |
| Unknown | 126 |
| food | |
| fasted | 136 (66%) |
| fed | 70 (34%) |
| site | |
| capillary | 86 (42%) |
| venous | 120 (58%) |
| Beta | 0.0022 (0.0019, 0.0026) |
| Unknown | 50 |
| Kel | 0.008 (0.006, 0.012) |
| Unknown | 50 |
| C_0_0_order | 0.93 (0.60, 1.00) |
| Unknown | 50 |
| V_d_0_order | 0.68 (0.60, 0.72) |
| Unknown | 50 |
| C_0_1_order | 1.64 (1.24, 2.15) |
| Unknown | 50 |
| V_d_1_order | 0.33 (0.24, 0.43) |
| Unknown | 50 |
| AUC | 142 (87, 196) |
| Unknown | 50 |
| Cmax | 0.85 (0.65, 1.20) |
| Tmax | 60 (43, 75) |
| Unknown | 50 |
| wt_kg | 75 (71, 82) |
| Unknown | 108 |
| sex | |
| F | 5 (3.4%) |
| M | 141 (97%) |
| Unknown | 60 |
| age | 32 (23, 61) |
| Unknown | 114 |
| bh_cm | 178 (172, 179) |
| Unknown | 164 |
| bmi | 23.6 (22.3, 25.2) |
| Unknown | 196 |
| bsa_m2 | 1.89 (1.83, 2.00) |
| Unknown | 124 |
| lbm_kg | 56 (51, 61) |
| Unknown | 156 |
| 1 n (%); Median (Q1, Q3) | |
1. ФК-параметры (endpoints)
• AUC, Cmax,
• C0_0, C0_1
• Beta, Kel,
• Tmax
• Vd_0,Vd_1
2. Условия эксперимента
• Dose (g/kg)
• Route (IV/PO)
• Occasion (1/2/NA)
• Food (fed/fasted)
• Infusion_duration_min
• Study_id
3. Личные данные пациента
• WT, BH, BMI, BSA, LBM
• Sex, Agemain_df_long <- main_df %>%
select(where(is.numeric)) %>%
pivot_longer(
cols = everything(),
names_to = "variable",
values_to = "value"
)
ggplot(main_df_long, aes(x = value)) +
geom_histogram(bins = 30) +
facet_wrap(~ variable, scales = "free") +
theme_bw() +
labs(x = NULL, y = NULL)## Warning: Removed 1262 rows containing non-finite outside the scale range
## (`stat_bin()`).
ggplot(main_df_long, aes(x = value)) +
geom_density(na.rm = TRUE) +
facet_wrap(~ variable, scales = "free") +
theme_bw() +
labs(x = NULL, y = NULL)
Можно прологорифмировать переменные с правосторонней ассиметрией Kel, V_d_o, V_d_1, C_0_1_order
main_df_fac_long <- main_df %>%
select(where(is.factor)) %>%
pivot_longer(
cols = everything(),
names_to = "variable",
values_to = "value"
)
ggplot(main_df_fac_long, aes(x = value)) +
geom_bar() +
facet_wrap(~ variable, scales = "free_x") +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(x = NULL, y = NULL)
pk_num <- main_df %>%
select(where(is.numeric)) %>%
select(-lbm_kg, -bsa_m2, -bh_cm, -bmi) #лишние убрал
cor_mat <- cor(pk_num, use = "pairwise.complete.obs")
cor_mat %>%
ggcorr(., label = TRUE)
Проверяем гипотезу о различиях групп по категориям food и site
H0: mean (fasted) = mean(fed) H1: mean (fasted) != mean(fed)
t.test(log(AUC) ~ food, data = order_0_df) ##
## Welch Two Sample t-test
##
## data: log(AUC) by food
## t = 9.009, df = 48.616, p-value = 6.134e-12
## alternative hypothesis: true difference in means between group fasted and group fed is not equal to 0
## 95 percent confidence interval:
## 0.4552847 0.7167806
## sample estimates:
## mean in group fasted mean in group fed
## 5.264317 4.678284t.test(log(Cmax) ~ food, data = order_0_df)##
## Welch Two Sample t-test
##
## data: log(Cmax) by food
## t = 10.867, df = 84.518, p-value < 2.2e-16
## alternative hypothesis: true difference in means between group fasted and group fed is not equal to 0
## 95 percent confidence interval:
## 0.4542268 0.6576812
## sample estimates:
## mean in group fasted mean in group fed
## 0.1532168 -0.4027372t.test(Tmax ~ food, data = order_0_df)##
## Welch Two Sample t-test
##
## data: Tmax by food
## t = -1.2786, df = 93.904, p-value = 0.2042
## alternative hypothesis: true difference in means between group fasted and group fed is not equal to 0
## 95 percent confidence interval:
## -16.571704 3.589098
## sample estimates:
## mean in group fasted mean in group fed
## 62.70968 69.20098t.test(Beta ~ food, data = order_0_df)##
## Welch Two Sample t-test
##
## data: Beta by food
## t = -0.18184, df = 39.468, p-value = 0.8566
## alternative hypothesis: true difference in means between group fasted and group fed is not equal to 0
## 95 percent confidence interval:
## -0.0002730811 0.0002280155
## sample estimates:
## mean in group fasted mean in group fed
## 0.002169623 0.002192156t.test(C_0_0_order ~ food, data = order_0_df)##
## Welch Two Sample t-test
##
## data: C_0_0_order by food
## t = 4.225, df = 37.009, p-value = 0.0001495
## alternative hypothesis: true difference in means between group fasted and group fed is not equal to 0
## 95 percent confidence interval:
## 0.09480385 0.26952408
## sample estimates:
## mean in group fasted mean in group fed
## 0.9811502 0.7989862t.test(V_d_0_order ~ food, data = order_0_df)##
## Welch Two Sample t-test
##
## data: V_d_0_order by food
## t = 1.5073, df = 46.389, p-value = 0.1385
## alternative hypothesis: true difference in means between group fasted and group fed is not equal to 0
## 95 percent confidence interval:
## -0.009793554 0.068237153
## sample estimates:
## mean in group fasted mean in group fed
## 0.6772378 0.6480160H0: mean (capillary) = mean(venous) H1: mean (capillary) != mean(venous)
t.test(log(AUC) ~ site, data = order_0_df) ##
## Welch Two Sample t-test
##
## data: log(AUC) by site
## t = 9.009, df = 48.616, p-value = 6.134e-12
## alternative hypothesis: true difference in means between group capillary and group venous is not equal to 0
## 95 percent confidence interval:
## 0.4552847 0.7167806
## sample estimates:
## mean in group capillary mean in group venous
## 5.264317 4.678284t.test(log(Cmax) ~ site, data = order_0_df)##
## Welch Two Sample t-test
##
## data: log(Cmax) by site
## t = -4.4454, df = 112.65, p-value = 2.065e-05
## alternative hypothesis: true difference in means between group capillary and group venous is not equal to 0
## 95 percent confidence interval:
## -0.3673048 -0.1408344
## sample estimates:
## mean in group capillary mean in group venous
## -0.1224289 0.1316407t.test(Tmax ~ site, data = order_0_df)##
## Welch Two Sample t-test
##
## data: Tmax by site
## t = -1.2786, df = 93.904, p-value = 0.2042
## alternative hypothesis: true difference in means between group capillary and group venous is not equal to 0
## 95 percent confidence interval:
## -16.571704 3.589098
## sample estimates:
## mean in group capillary mean in group venous
## 62.70968 69.20098t.test(Beta ~ site, data = order_0_df)##
## Welch Two Sample t-test
##
## data: Beta by site
## t = -0.18184, df = 39.468, p-value = 0.8566
## alternative hypothesis: true difference in means between group capillary and group venous is not equal to 0
## 95 percent confidence interval:
## -0.0002730811 0.0002280155
## sample estimates:
## mean in group capillary mean in group venous
## 0.002169623 0.002192156t.test(C_0_0_order ~ site, data = order_0_df)##
## Welch Two Sample t-test
##
## data: C_0_0_order by site
## t = 4.225, df = 37.009, p-value = 0.0001495
## alternative hypothesis: true difference in means between group capillary and group venous is not equal to 0
## 95 percent confidence interval:
## 0.09480385 0.26952408
## sample estimates:
## mean in group capillary mean in group venous
## 0.9811502 0.7989862t.test(V_d_0_order ~ site, data = order_0_df)##
## Welch Two Sample t-test
##
## data: V_d_0_order by site
## t = 1.5073, df = 46.389, p-value = 0.1385
## alternative hypothesis: true difference in means between group capillary and group venous is not equal to 0
## 95 percent confidence interval:
## -0.009793554 0.068237153
## sample estimates:
## mean in group capillary mean in group venous
## 0.6772378 0.6480160Проверяем гипотезу о различиях групп по категориям route и sex.
t.test(Beta ~ route, data = order_0_df) ##
## Welch Two Sample t-test
##
## data: Beta by route
## t = -3.952, df = 22.053, p-value = 0.0006754
## alternative hypothesis: true difference in means between group IV and group PO is not equal to 0
## 95 percent confidence interval:
## -0.0007806703 -0.0002433704
## sample estimates:
## mean in group IV mean in group PO
## 0.001761587 0.002273607t.test(Beta ~ sex, data = order_0_df)##
## Welch Two Sample t-test
##
## data: Beta by sex
## t = 4.5937, df = 4.4652, p-value = 0.007747
## alternative hypothesis: true difference in means between group F and group M is not equal to 0
## 95 percent confidence interval:
## 0.0003360822 0.0012656766
## sample estimates:
## mean in group F mean in group M
## 0.002982604 0.002181725t.test(V_d_0_order ~ route, data = order_0_df) ##
## Welch Two Sample t-test
##
## data: V_d_0_order by route
## t = -4.1909, df = 29.494, p-value = 0.0002314
## alternative hypothesis: true difference in means between group IV and group PO is not equal to 0
## 95 percent confidence interval:
## -0.10884915 -0.03748701
## sample estimates:
## mean in group IV mean in group PO
## 0.6074394 0.6806074t.test(V_d_0_order ~ sex, data = order_0_df)##
## Welch Two Sample t-test
##
## data: V_d_0_order by sex
## t = -3.2625, df = 4.6626, p-value = 0.02478
## alternative hypothesis: true difference in means between group F and group M is not equal to 0
## 95 percent confidence interval:
## -0.17872778 -0.01926724
## sample estimates:
## mean in group F mean in group M
## 0.5752923 0.6742898t.test(log(AUC) ~ route, data = order_0_df) ##
## Welch Two Sample t-test
##
## data: log(AUC) by route
## t = -7.8695, df = 25.189, p-value = 3.004e-08
## alternative hypothesis: true difference in means between group IV and group PO is not equal to 0
## 95 percent confidence interval:
## -0.7934900 -0.4644088
## sample estimates:
## mean in group IV mean in group PO
## 4.545742 5.174692t.test(log(AUC) ~ sex, data = order_0_df)##
## Welch Two Sample t-test
##
## data: log(AUC) by sex
## t = -4.0071, df = 4.3155, p-value = 0.01379
## alternative hypothesis: true difference in means between group F and group M is not equal to 0
## 95 percent confidence interval:
## -1.1037583 -0.2154553
## sample estimates:
## mean in group F mean in group M
## 4.564367 5.223974t.test(Cmax ~ route, data = order_0_df)##
## Welch Two Sample t-test
##
## data: Cmax by route
## t = 7.9694, df = 80.102, p-value = 9.216e-12
## alternative hypothesis: true difference in means between group IV and group PO is not equal to 0
## 95 percent confidence interval:
## 0.3849115 0.6411207
## sample estimates:
## mean in group IV mean in group PO
## 1.3830588 0.8700427t.test(Cmax ~ sex, data = order_0_df)##
## Welch Two Sample t-test
##
## data: Cmax by sex
## t = -5.4861, df = 5.6395, p-value = 0.001876
## alternative hypothesis: true difference in means between group F and group M is not equal to 0
## 95 percent confidence interval:
## -0.7970139 -0.3000288
## sample estimates:
## mean in group F mean in group M
## 0.668000 1.216521t.test(Tmax ~ route, data = order_0_df)##
## Welch Two Sample t-test
##
## data: Tmax by route
## t = 2.5439, df = 24.323, p-value = 0.01772
## alternative hypothesis: true difference in means between group IV and group PO is not equal to 0
## 95 percent confidence interval:
## 3.588895 34.338455
## sample estimates:
## mean in group IV mean in group PO
## 80.41667 61.45299t.test(Tmax ~ sex, data = order_0_df)##
## Welch Two Sample t-test
##
## data: Tmax by sex
## t = -0.032711, df = 4.5118, p-value = 0.9753
## alternative hypothesis: true difference in means between group F and group M is not equal to 0
## 95 percent confidence interval:
## -43.19236 42.14162
## sample estimates:
## mean in group F mean in group M
## 63.40000 63.92537Результаты: Группы по способу введения различаются по всем фк параметрам. Группы по полу различаются по всем фк-параметрам кроме Tmax.